






import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler



@dataclass
class 				_A			(     UpperCamelCase				):
    """simple docstring"""

    lowerCamelCase	:							int				= 0
    lowerCamelCase	:							bool				= False
    lowerCamelCase	:							float				= 3.0




class 				_A			(     unittest.TestCase				):
    """simple docstring"""

    def _a							(				self      :     Tuple      )      ->							Dict:
           # If no defaults are changed, `to_kwargs` returns an empty dict.
           self.assertDictEqual(MockClass().to_kwargs() ,     {}      )
           self.assertDictEqual(MockClass(a=2      ).to_kwargs() ,     {"""a""": 2}      )
           self.assertDictEqual(MockClass(a=2 ,     b=__SCREAMING_SNAKE_CASE      ).to_kwargs() ,     {"""a""": 2, """b""": True}      )
           self.assertDictEqual(MockClass(a=2 ,     c=2.25      ).to_kwargs() ,     {"""a""": 2, """c""": 2.25}      )



    @require_cuda
    def _a							(				self      :     str      )      ->							Optional[Any]:
           # If no defaults are changed, `to_kwargs` returns an empty dict.
           __UpperCAmelCase     				=GradScalerKwargs(init_scale=1024 ,     growth_factor=2      )
           AcceleratorState._reset_state()
           __UpperCAmelCase     				=Accelerator(mixed_precision="""fp16""" ,     kwargs_handlers=[scaler_handler]      )
           print(accelerator.use_fpaa      )
           __UpperCAmelCase     				=accelerator.scaler

           # Check the kwargs have been applied
           self.assertEqual(scaler._init_scale ,     1_024.0      )
           self.assertEqual(scaler._growth_factor ,     2.0      )

           # Check the other values are at the default
           self.assertEqual(scaler._backoff_factor ,     0.5      )
           self.assertEqual(scaler._growth_interval ,     2000      )
           self.assertEqual(scaler._enabled ,     __SCREAMING_SNAKE_CASE      )



    @require_multi_gpu
    def _a							(				self      :     Optional[Any]      )      ->							Optional[Any]:
           __UpperCAmelCase     				=["""torchrun""", f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__      )]
           execute_subprocess_async(__SCREAMING_SNAKE_CASE ,     env=os.environ.copy()      )


if __name__ == "__main__":
   __A					=	DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
   __A					=	Accelerator(kwargs_handlers=[ddp_scaler])
   __A					=	torch.nn.Linear(1_00, 2_00)
   __A					=	accelerator.prepare(model)

   # Check the values changed in kwargs
   __A					=	""
   __A					=	model.bucket_bytes_cap // (10_24 * 10_24)
   if observed_bucket_cap_map != 15:
      error_msg += F"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
   if model.find_unused_parameters is not True:
      error_msg += F"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

   # Check the values of the defaults
   if model.dim != 0:
      error_msg += F"Default value not respected, should have `0` but found {model.dim}.\n"
   if model.broadcast_buffers is not True:
      error_msg += F"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
   if model.gradient_as_bucket_view is not False:
      error_msg += F"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

   # Raise error at the end to make sure we don't stop at the first failure.
   if len(error_msg) > 0:
      raise ValueError(error_msg)





import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
__a    :	Union[str, Any]         =    logging.get_logger(__name__)





def 							UpperCAmelCase		(       lowercase				):







				"""simple docstring"""



				__lowercase					     =				torch.load(lowercase				,     map_location='''cpu'''				)
				if "model" in sd.keys():
								__lowercase					     =				torch.load(lowercase				,     map_location='''cpu'''				)['''model''']

				# pop unnecessary weights
				__lowercase					     =				[
				    '''decoder.version''',
				    '''decoder.output_projection.weight''',
				]
				for key in keys_to_delete:
								if key in sd:
												sd.pop(lowercase				)

				__lowercase					     =				{
				    '''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
				    '''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
				    '''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
				    '''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
				}
				for old_key, new_key in keys_to_rename.items():
								if old_key in sd:
												__lowercase					     =				sd.pop(lowercase				)

				__lowercase					     =				list(sd.keys()				)
				for key in keys:
								if ".qkv_proj." in key:
												__lowercase					     =				sd[key]
												# We split QKV in separate Q,K,V

												__lowercase					     =				key.replace('''.qkv_proj.'''				,     '''.q_proj.'''				)
												__lowercase					     =				key.replace('''.qkv_proj.'''				,     '''.k_proj.'''				)
												__lowercase					     =				key.replace('''.qkv_proj.'''				,     '''.v_proj.'''				)

												__lowercase					     =				value.shape[0]
												assert depth % 3 == 0
												# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
												# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
												__lowercase     ,	__lowercase     ,	__lowercase					     =				torch.split(lowercase				,     depth // 3				,     dim=0				)

												__lowercase					     =				q
												__lowercase					     =				k
												__lowercase					     =				v
												del sd[key]

				return sd





@torch.no_grad()
def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase=None				):







				"""simple docstring"""



				__lowercase					     =				load_checkpoint(lowercase				)

				if config is not None:
								__lowercase					     =				OPTConfig.from_pretrained(lowercase				)
				else:
								__lowercase					     =				OPTConfig()

				__lowercase					     =				OPTModel(lowercase				).half().eval()
				model.load_state_dict(lowercase				)

				# Check results
				Path(lowercase				).mkdir(exist_ok=lowercase				)
				model.save_pretrained(lowercase				)


if __name__ == "__main__":
		__a    :	int         =    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--fairseq_path""",
		    type=str,
		    help=(
		        """path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"""
		        """ https://huggingface.co/models?other=opt_metasq"""
		    ),
		)
		parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
		parser.add_argument("""--hf_config""", default=None, type=str, help="""Define HF config.""")
		__a    :	Any         =    parser.parse_args()
		convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)